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STATUS:CONFIRMED
LAST-MODIFIED:20140501T231511
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UID:ATEvent-eed436ece7e23bbcfe0d4623c4969225
SUMMARY:ARC Colloquium: Vitaly Feldman\, IBM Almaden Research Center\, San Jose\, CA.
DESCRIPTION:Title: Statistical Algorithms and a Lower Bound for Detecting Planted Cliques\nAbstract:\nWe introduce a framework for proving lower bounds on computational problems over distributions\, based on a class of algorithms called statistical algorithms. For such algorithms\, access to the input distribution is limited to obtaining an estimate of the expectation of any given function on a sample drawn randomly from the input distribution\, rather than directly accessing samples. Most natural algorithms of interest in theory and in practice\, e.g.\, moments-based methods\, local search\, standard iterative methods for convex optimization\, MCMC and simulated annealing\, are statistical algorithms or have statistical counterparts. Our framework is inspired by and generalizes the statistical query model in learning theory.\nOur main application is a nearly optimal lower bound on the complexity of any statistical algorithm for detecting planted bipartite clique distributions (or planted dense subgraph distributions) when the planted clique has size O(n^(1/2-\delta)) for any constant \delta > 0. Variants of these problems have been assumed to be hard to prove hardness for other problems and for cryptographic applications. Our lower bounds provide concrete evidence of hardness\, thus supporting these assumptions.\nJoint work with Elena Grigorescu\, Lev Reyzin\, Santosh Vempala and Ying Xiao\n \n
DTSTART:20130501T130000
DTEND:20130501T130000
CREATED:20130424T111510
DTSTAMP:20130424T111510
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